An improved method using kernel density estimation （KDE） and confidence level is presented for model validation with small samples. Decision making is a challenging problem because of input uncertainty and only smal...An improved method using kernel density estimation （KDE） and confidence level is presented for model validation with small samples. Decision making is a challenging problem because of input uncertainty and only small samples can be used due to the high costs of experimental measurements. However, model validation provides more confidence for decision makers when improving prediction accuracy at the same time. The confidence level method is introduced and the optimum sample variance is determined using a new method in kernel density estimation to increase the credibility of model validation. As a numerical example, the static frame model validation challenge problem presented by Sandia National Laboratories has been chosen. The optimum bandwidth is selected in kernel density estimation in order to build the probability model based on the calibration data. The model assessment is achieved using validation and accreditation experimental data respectively based on the probability model. Finally, the target structure prediction is performed using validated model, which are consistent with the results obtained by other researchers. The results demonstrate that the method using the improved confidence level and kernel density estimation is an effective approach to solve the model validation problem with small samples.展开更多
Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model ...Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.展开更多
The application of support vector machines to forecasting problems is becoming popular, lately. Several comparisons between neural networks trained with error backpropagation and support vector machines have shown adv...The application of support vector machines to forecasting problems is becoming popular, lately. Several comparisons between neural networks trained with error backpropagation and support vector machines have shown advantage for the latter in different domains of application. However, some difficulties still deteriorate the performance of the support vector machines. The main one is related to the setting of the hyperparameters involved in their training. Techniques based on meta-heuristics have been employed to determine appropriate values for those hyperparameters. However, because of the high noneonvexity of this estimation problem, which makes the search for a good solution very hard, an approach based on Bayesian inference, called relevance vector machine, has been proposed more recently. The present paper aims at investigating the suitability of this new approach to the short-term load forecasting problem.展开更多
Landslide susceptibility mapping is significant for landslide prevention.Many approaches have been used for landslide susceptibility prediction,however,their performances are unstable.This study constructed a hybrid m...Landslide susceptibility mapping is significant for landslide prevention.Many approaches have been used for landslide susceptibility prediction,however,their performances are unstable.This study constructed a hybrid model,namely box counting dimension-based kernel logistic regression model,which uses fractal dimension calculated by box counting method as input data based on grid cells mapping unit and terrain mapping unit.The performance of this model was evaluated in the application in Zhidan County,Shaanxi Province,China.Firstly,a total of 221 landslides were identified and mapped,and 11 landslide predisposing factors were considered.Secondly,the landslide susceptibility maps(LSMs) of the study area were obtained by constructing the model on two different mapping units.Finally,the results were evaluated with five statistical indexes,sensitivity,specificity,positive predictive value(PPV),negative predictive value(NPV) and Accuracy.The statistical indexes of the model obtained on the terrain mapping unit were larger than those based on grid cells mapping unit.For training and validation datasets,the area under the receiver operating characteristic curve(AUC) of the model based on terrain mapping unit were 0.9374 and 0.9527,respectively,indicating that establishing this model on the terrain mapping unit was advantageous in the study area.The results show that the fractal dimension improves the prediction ability of the kernel logistic model.In addition,the terrain mapping unit is a more promising mapping unit in Loess areas.展开更多
The understanding of the mechanism for the mass building of elementary particles of Standard Model (SM) has made significant progresses since the confirmation of the existence of the Higgs boson, in particular the rea...The understanding of the mechanism for the mass building of elementary particles of Standard Model (SM) has made significant progresses since the confirmation of the existence of the Higgs boson, in particular the realization that the mass of an elementary particle of SM is not “God-given” but is created by interactions with involved energy fields. Nevertheless, a sophisticated model to answer fundamental questions is still missing. Further research is needed to compensate for the existing deficit. The current paper is aimed to contribute to such research by using “harmonic quark series”. Harmonic quark series were introduced between 2003 and 2005 by O. A. Teplov and represented a relatively new approach to understanding the physical masses of elementary particles. Although they are not generally recognized, some research works have revealed very interesting and exciting facts regarding the mass quanta. The original harmonic quark series consists of mathematical “quark” entities with an energy-mass quantum between 7.87 MeV and 69.2 GeV. They obey a strict mathematical rule derived from the general harmonic oscillation theory. Teplov showed some quantitative relations between the masses of his harmonic quarks and the SM particles, especially in the intermediate mass range, i.e. mesons and hadrons up to 1000 MeV. Early research work also includes the investigation of H. Yang/W. Yang in the development of their so-called YY model for elementary particles (Ying-Yang model with “Ying” and “Yang” as quark components for a new theoretical particle framework). Based on Teplov’s scheme and its mathematical formula, they introduced further harmonic quarks down to 1 eV and showed some quantitative relationships between the masses of these harmonic quarks and the masses of electrons and up and down quarks. In this article, we will extend the harmonic quark series according to the Teplov scheme up to a new entity with a mass quantum of 253.4 GeV and show some interesting new mass relations to the heavy particles of the Standard Model (W boson, Z boson, top quark and Higgs boson). Based on these facts, some predictions will be made for experimental verification. We also hope that our investigation and result will motivate more researcher to dedicate their work to harmonic quark series in theory and in experiments.展开更多
Land use/cover change(LUCC)is a measure that offers insights into the interaction between human activities and the natural environment,which significantly impacts the ecological environment of a region.Based on data f...Land use/cover change(LUCC)is a measure that offers insights into the interaction between human activities and the natural environment,which significantly impacts the ecological environment of a region.Based on data from the period from 2000 to 2020 regarding land use,topography,climate,the economy,and population,this study investigates the spatial and temporal evolution of land use in the Liuchong River Basin,examining the inte-raction between human activities and the natural environment using the land use dynamics model,the transfer matrix model,the kernel density model,and the geodetic detector.The results indicate that:(1)The type of land cover in Liuchong River Basin primarily comprises cropland,forest,and shrubs,with the land use change mode mainly consisting of an increase in the impervious area and a decrease in surface area covered by shrubs.(2)The dynamic degree for single land use of barren,impervious,and waters indicates a significant increase,with areas covered by shrubs decreasing by 9.37%.In addition,the change in the degree of single land use for other types of cover is more stable,with the degree of comprehensive land use being 7.95%.The areas experiencing the greatest land use change in the watershed went through conditions that can be described as“sporadic distribution”to“dis-persed”to“relatively concentrated”.(3)Air temperature,rainfall,and elevation are important factors driving land use changes in the Liuchong River Basin.The impact of nighttime lighting,gross domestic product(GDP),and norma-lized difference vegetation index(NDVI)on land use change have gradually increased over time.The results of the interaction detection indicated that the explanatory power of the interaction between the driving factors in each pe-riod for land-use changes was always greater than that of any single factor.The results of this study offer evi-dence-based support and scientific references for spatial planning,soil and water conservation,and ecological restoration in a watershed.展开更多
The feature information of the local graph structure and the nodes may be over-smoothing due to the large number of encodings,which causes the node characterization to converge to one or several values.In other words,...The feature information of the local graph structure and the nodes may be over-smoothing due to the large number of encodings,which causes the node characterization to converge to one or several values.In other words,nodes from different clusters become difficult to distinguish,as two different classes of nodes with closer topological distance are more likely to belong to the same class and vice versa.To alleviate this problem,an over-smoothing algorithm is proposed,and a method of reweighted mechanism is applied to make the tradeoff of the information representation of nodes and neighborhoods more reasonable.By improving several propagation models,including Chebyshev polynomial kernel model and Laplace linear 1st Chebyshev kernel model,a new model named RWGCN based on different propagation kernels was proposed logically.The experiments show that satisfactory results are achieved on the semi-supervised classification task of graph type data.展开更多
基金Funding of Jiangsu Innovation Program for Graduate Education (CXZZ11_0193)NUAA Research Funding (NJ2010009)
文摘An improved method using kernel density estimation （KDE） and confidence level is presented for model validation with small samples. Decision making is a challenging problem because of input uncertainty and only small samples can be used due to the high costs of experimental measurements. However, model validation provides more confidence for decision makers when improving prediction accuracy at the same time. The confidence level method is introduced and the optimum sample variance is determined using a new method in kernel density estimation to increase the credibility of model validation. As a numerical example, the static frame model validation challenge problem presented by Sandia National Laboratories has been chosen. The optimum bandwidth is selected in kernel density estimation in order to build the probability model based on the calibration data. The model assessment is achieved using validation and accreditation experimental data respectively based on the probability model. Finally, the target structure prediction is performed using validated model, which are consistent with the results obtained by other researchers. The results demonstrate that the method using the improved confidence level and kernel density estimation is an effective approach to solve the model validation problem with small samples.
基金Supported partially by the Post Doctoral Natural Science Foundation of China(2013M532118,2015T81082)the National Natural Science Foundation of China(61573364,61273177,61503066)+2 种基金the State Key Laboratory of Synthetical Automation for Process Industriesthe National High Technology Research and Development Program of China(2015AA043802)the Scientific Research Fund of Liaoning Provincial Education Department(L2013272)
文摘Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.
文摘The application of support vector machines to forecasting problems is becoming popular, lately. Several comparisons between neural networks trained with error backpropagation and support vector machines have shown advantage for the latter in different domains of application. However, some difficulties still deteriorate the performance of the support vector machines. The main one is related to the setting of the hyperparameters involved in their training. Techniques based on meta-heuristics have been employed to determine appropriate values for those hyperparameters. However, because of the high noneonvexity of this estimation problem, which makes the search for a good solution very hard, an approach based on Bayesian inference, called relevance vector machine, has been proposed more recently. The present paper aims at investigating the suitability of this new approach to the short-term load forecasting problem.
基金funded by National Key Research and Development Program of China, Ecological Safety Guarantee Technology and Demonstration Channel and Slope Treatment Project in Loess Hilly and Gully Area (Grant No. 2017YFC0504700)。
文摘Landslide susceptibility mapping is significant for landslide prevention.Many approaches have been used for landslide susceptibility prediction,however,their performances are unstable.This study constructed a hybrid model,namely box counting dimension-based kernel logistic regression model,which uses fractal dimension calculated by box counting method as input data based on grid cells mapping unit and terrain mapping unit.The performance of this model was evaluated in the application in Zhidan County,Shaanxi Province,China.Firstly,a total of 221 landslides were identified and mapped,and 11 landslide predisposing factors were considered.Secondly,the landslide susceptibility maps(LSMs) of the study area were obtained by constructing the model on two different mapping units.Finally,the results were evaluated with five statistical indexes,sensitivity,specificity,positive predictive value(PPV),negative predictive value(NPV) and Accuracy.The statistical indexes of the model obtained on the terrain mapping unit were larger than those based on grid cells mapping unit.For training and validation datasets,the area under the receiver operating characteristic curve(AUC) of the model based on terrain mapping unit were 0.9374 and 0.9527,respectively,indicating that establishing this model on the terrain mapping unit was advantageous in the study area.The results show that the fractal dimension improves the prediction ability of the kernel logistic model.In addition,the terrain mapping unit is a more promising mapping unit in Loess areas.
文摘The understanding of the mechanism for the mass building of elementary particles of Standard Model (SM) has made significant progresses since the confirmation of the existence of the Higgs boson, in particular the realization that the mass of an elementary particle of SM is not “God-given” but is created by interactions with involved energy fields. Nevertheless, a sophisticated model to answer fundamental questions is still missing. Further research is needed to compensate for the existing deficit. The current paper is aimed to contribute to such research by using “harmonic quark series”. Harmonic quark series were introduced between 2003 and 2005 by O. A. Teplov and represented a relatively new approach to understanding the physical masses of elementary particles. Although they are not generally recognized, some research works have revealed very interesting and exciting facts regarding the mass quanta. The original harmonic quark series consists of mathematical “quark” entities with an energy-mass quantum between 7.87 MeV and 69.2 GeV. They obey a strict mathematical rule derived from the general harmonic oscillation theory. Teplov showed some quantitative relations between the masses of his harmonic quarks and the SM particles, especially in the intermediate mass range, i.e. mesons and hadrons up to 1000 MeV. Early research work also includes the investigation of H. Yang/W. Yang in the development of their so-called YY model for elementary particles (Ying-Yang model with “Ying” and “Yang” as quark components for a new theoretical particle framework). Based on Teplov’s scheme and its mathematical formula, they introduced further harmonic quarks down to 1 eV and showed some quantitative relationships between the masses of these harmonic quarks and the masses of electrons and up and down quarks. In this article, we will extend the harmonic quark series according to the Teplov scheme up to a new entity with a mass quantum of 253.4 GeV and show some interesting new mass relations to the heavy particles of the Standard Model (W boson, Z boson, top quark and Higgs boson). Based on these facts, some predictions will be made for experimental verification. We also hope that our investigation and result will motivate more researcher to dedicate their work to harmonic quark series in theory and in experiments.
基金The National Natural Science Foundation of China (U1812401)The Science and Technology Support Plan in Guizhou Province (G[2020]4Y016)+1 种基金The 2019 Philosophy and Social Science Planning Key Topics in Guizhou Province (19GZZD07)The Guizhou Provincial Water Resources Science and Technology Funding Program (KT202108)。
文摘Land use/cover change(LUCC)is a measure that offers insights into the interaction between human activities and the natural environment,which significantly impacts the ecological environment of a region.Based on data from the period from 2000 to 2020 regarding land use,topography,climate,the economy,and population,this study investigates the spatial and temporal evolution of land use in the Liuchong River Basin,examining the inte-raction between human activities and the natural environment using the land use dynamics model,the transfer matrix model,the kernel density model,and the geodetic detector.The results indicate that:(1)The type of land cover in Liuchong River Basin primarily comprises cropland,forest,and shrubs,with the land use change mode mainly consisting of an increase in the impervious area and a decrease in surface area covered by shrubs.(2)The dynamic degree for single land use of barren,impervious,and waters indicates a significant increase,with areas covered by shrubs decreasing by 9.37%.In addition,the change in the degree of single land use for other types of cover is more stable,with the degree of comprehensive land use being 7.95%.The areas experiencing the greatest land use change in the watershed went through conditions that can be described as“sporadic distribution”to“dis-persed”to“relatively concentrated”.(3)Air temperature,rainfall,and elevation are important factors driving land use changes in the Liuchong River Basin.The impact of nighttime lighting,gross domestic product(GDP),and norma-lized difference vegetation index(NDVI)on land use change have gradually increased over time.The results of the interaction detection indicated that the explanatory power of the interaction between the driving factors in each pe-riod for land-use changes was always greater than that of any single factor.The results of this study offer evi-dence-based support and scientific references for spatial planning,soil and water conservation,and ecological restoration in a watershed.
文摘The feature information of the local graph structure and the nodes may be over-smoothing due to the large number of encodings,which causes the node characterization to converge to one or several values.In other words,nodes from different clusters become difficult to distinguish,as two different classes of nodes with closer topological distance are more likely to belong to the same class and vice versa.To alleviate this problem,an over-smoothing algorithm is proposed,and a method of reweighted mechanism is applied to make the tradeoff of the information representation of nodes and neighborhoods more reasonable.By improving several propagation models,including Chebyshev polynomial kernel model and Laplace linear 1st Chebyshev kernel model,a new model named RWGCN based on different propagation kernels was proposed logically.The experiments show that satisfactory results are achieved on the semi-supervised classification task of graph type data.
